用预训练3DCNN+SVM实现老人跌倒自动检测
Human Fall Detection using Transfer Learning-based 3D CNN
- 用预训练3D CNN提取动作时空特征，仅训练SVM分类器
- 在两个数据集上达到97.8%和96.4%的准确率
- 适合做轻量级跌倒监测系统，代码开源
意外跌倒对老年人是重大健康问题，随着老龄人口增长，亟需自动化跌倒监测系统。本文提出一种基于视觉的跌倒检测方法，采用在Sports1M数据集上预训练的3D CNN模型，可同时提取空间与时间特征。仅训练SVM分类器，大幅节省训练时间。通过分层随机五折交叉验证划分数据集，使用GMDCSA与CAUCAFall两个数据集进行实验。从3D CNN提取的特征输入SVM，实现跌倒与日常活动（ADL）的分类。源代码可在GitHub公开获取。
原文摘要 · Abstract (English)
Unintentional or accidental falls are one of the significant health issues in senior persons. The population of senior persons is increasing steadily. So, there is a need for an automated fall detection monitoring system. This paper introduces a vision-based fall detection system using a pre-trained 3D CNN. Unlike 2D CNN, 3D CNN extracts not only spatial but also temporal features. The proposed model leverages the original learned weights of a 3D CNN model pre-trained on the Sports1M dataset to extract the spatio-temporal features. Only the SVM classifier was trained, which saves the time required to train the 3D CNN. Stratified shuffle five split cross-validation has been used to split the dataset into training and testing data. Extracted features from the proposed 3D CNN model were fed to an SVM classifier to classify the activity as fall or ADL. Two datasets, GMDCSA and CAUCAFall, were utilized to conduct the experiment. The source code for this work can be accessed via the following link: https://github.com/ekramalam/HFD_3DCNN.
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